import tensorflow as tf


# LeNet5模型

def inference(images, batch_size, n_classes):
    # conv1, shape = [kernel size, kernel size, channels, kernel numbers]第一层卷积

    with tf.variable_scope('conv1') as scope:
        weights = tf.get_variable('weights',
                                  shape=[3, 3, 3, 16],  # 卷积尺寸3*3，通道3 ，深度16
                                  dtype=tf.float32,
                                  initializer=tf.truncated_normal_initializer(stddev=0.1, dtype=tf.float32))
        biases = tf.get_variable('biases',
                                 shape=[16],  # 深度16
                                 dtype=tf.float32,
                                 initializer=tf.constant_initializer(0.1))  # 0.1填充
        conv = tf.nn.conv2d(images, weights, strides=[1, 1, 1, 1], padding='SAME')  # 步长为1的过滤器
        pre_activation = tf.nn.bias_add(conv, biases)
        conv1 = tf.nn.relu(pre_activation, name=scope.name)

    # pool1 and norm1池化层
    with tf.variable_scope('pooling1_lrn') as scope:
        pool1 = tf.nn.max_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1],
                               padding='SAME', name='pooling1')
        norm1 = tf.nn.lrn(pool1, depth_radius=4, bias=1.0, alpha=0.001 / 9.0,
                          beta=0.75, name='norm1')

    # conv2
    with tf.variable_scope('conv2') as scope:
        weights = tf.get_variable('weights',
                                  shape=[3, 3, 16, 16],  # 3*3大小，16第一层深度，16第二层深度
                                  dtype=tf.float32,
                                  initializer=tf.truncated_normal_initializer(stddev=0.1, dtype=tf.float32))
        biases = tf.get_variable('biases',
                                 shape=[16],  # 第二层的深度为16
                                 dtype=tf.float32,
                                 initializer=tf.constant_initializer(0.1))
        conv = tf.nn.conv2d(norm1, weights, strides=[1, 1, 1, 1], padding='SAME')
        pre_activation = tf.nn.bias_add(conv, biases)
        conv2 = tf.nn.relu(pre_activation, name='conv2')

    # pool2 and norm2第二层卷积的池化层
    with tf.variable_scope('pooling2_lrn') as scope:
        norm2 = tf.nn.lrn(conv2, depth_radius=4, bias=1.0, alpha=0.001 / 9.0,
                          beta=0.75, name='norm2')
        pool2 = tf.nn.max_pool(norm2, ksize=[1, 3, 3, 1], strides=[1, 1, 1, 1],
                               padding='SAME', name='pooling2')

    # local3将上一层的池化层的输出转为这一层全连接层的输入格式
    with tf.variable_scope('local3') as scope:
        reshape = tf.reshape(pool2, shape=[batch_size, -1])
        dim = reshape.get_shape()[1].value
        weights = tf.get_variable('weights',
                                  shape=[dim, 128],
                                  dtype=tf.float32,
                                  initializer=tf.truncated_normal_initializer(stddev=0.005, dtype=tf.float32))
        biases = tf.get_variable('biases',
                                 shape=[128],
                                 dtype=tf.float32,
                                 initializer=tf.constant_initializer(0.1))
        local3 = tf.nn.relu(tf.matmul(reshape, weights) + biases, name=scope.name)

    # local4
    with tf.variable_scope('local4') as scope:
        weights = tf.get_variable('weights',
                                  shape=[128, 128],
                                  dtype=tf.float32,
                                  initializer=tf.truncated_normal_initializer(stddev=0.005, dtype=tf.float32))
        biases = tf.get_variable('biases',
                                 shape=[128],
                                 dtype=tf.float32,
                                 initializer=tf.constant_initializer(0.1))
        local4 = tf.nn.relu(tf.matmul(local3, weights) + biases, name='local4')

    # softmax
    with tf.variable_scope('softmax_linear') as scope:
        weights = tf.get_variable('softmax_linear',
                                  shape=[128, n_classes],
                                  dtype=tf.float32,
                                  initializer=tf.truncated_normal_initializer(stddev=0.005, dtype=tf.float32))
        biases = tf.get_variable('biases',
                                 shape=[n_classes],
                                 dtype=tf.float32,
                                 initializer=tf.constant_initializer(0.1))
        softmax_linear = tf.add(tf.matmul(local4, weights), biases, name='softmax_linear')

    return softmax_linear


def losses(logits, labels):
    """
    Compute loss from logits and labels
    Args:
        logits: logits tensor, float, [batch_size, n_classes]
        labels: label tensor, tf.int32, [batch_size]
    Returns:
        loss tensor of float type
    """
    with tf.variable_scope('loss') as scope:
        # 定义交叉熵
        cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits \
            (logits=logits, labels=labels, name='xentropy_per_example')
        # 求交叉熵损失值
        loss = tf.reduce_mean(cross_entropy, name='loss')
        # board相关，添加标量统计结果
        tf.summary.scalar(scope.name + '/loss', loss)
    return loss


def trainning(loss, learning_rate):
    """
    Training ops, the Op returned by this function is what must be passed to
        'sess.run()' call to cause the model to train.
    Args:
        loss: loss tensor, from losses()
    Returns:
        train_op: The op for trainning
    """
    with tf.name_scope('optimizer'):
        # Adam优化算法，代入学习率
        optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
        global_step = tf.Variable(0, name='global_step', trainable=False)
        #优化器优化loss
        train_op = optimizer.minimize(loss, global_step=global_step)
    return train_op


def evaluation(logits, labels):
    """
    验证准确度
    Evaluate the quality of the logits at predicting the label.
    Args:
        logits: Logits tensor, float - [batch_size, NUM_CLASSES].
        labels: Labels tensor, int32 - [batch_size], with values in the
        range [0, NUM_CLASSES).
    Returns:
        A scalar int32 tensor with the number of examples (out of batch_size)
        that were predicted correctly.
    """
    with tf.variable_scope('accuracy') as scope:
        # tf.nn.in_top_k组要是用于计算预测的结果和实际结果的是否相等，
        # 返回一个bool类型的张量，tf.nn.in_top_k(prediction, target, K):prediction就是表示你预测的结果，
        # 大小就是预测样本的数量乘以输出的维度，类型是tf.float32等。target就是实际样本类别的标签，大小就是样本数量的个数。
        # K表示每个样本的预测结果的前K个l最大的数里面是否含有target中的值。一般都是取1。
        # in_top_k(predictions, targets, k, name=None)用于计算预测的结果和实际结果的是否相等
        # predictions：预测的结果，预测矩阵大小为样本数×标注的label类的个数的二维矩阵
        # targets：实际的标签，大小为样本数。
        # k：每个样本的预测结果的前k个最大的数里面是否包含targets预测中的标签，一般都是取1，即取预测最大概率的索引与标签对比
        correct = tf.nn.in_top_k(logits, labels, 1)
        # 转换类型
        correct = tf.cast(correct, tf.float16)
        # 取平均值
        accuracy = tf.reduce_mean(correct)
        # 对标量数据汇总和记录,TensorBoard相关
        tf.summary.scalar(scope.name + '/accuracy', accuracy)
    return accuracy
